Deep Layer Neural Networks (DLNNs) utilize standard feedforward and back propagation processes to take information from datasets (usually visual) and classify them in a set of predefined bins utilizing both unsupervised and supervised learning. However, DLNN systems are so complex that it is not apparent what details in the data are being used to classify the image, and subtle changes in data values can drastically change the classification. This reductionist “black box” approach also does not enable new or creative datasets to be developed based on prior datasets. Accordingly, an improved approach may be beneficial.